Generic Model Research Articles

Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules

Chemical derivatization is a powerful strategy to enhance sensitivity and selectivity of liquid chromatography-mass spectrometry for non-targeted analysis of chemicals in complex mixtures. However, it remains impossible to obtain large sets of reference spectra for chemically derived molecules (CDMs), representing a major barrier in real-world applications. Herein, we describe a deep learning approach that enables accurate prediction of electrospray ionization tandem mass spectra for CDMs (DeepCDM). DeepCDM is established by transfer learning from a generic spectrum predicting model using a small set of experimentally acquired tandem mass spectra of CDMs, which converts a generic model with low predictability for CDMs into a specialized model with high predictability. We demonstrate DeepCDM by predicting electrospray ionization tandem mass spectra of dansylated molecules. The success in establishing Dns-MS further enables the development of DnsBank, a dansylation-specialized in silico spectral library. DnsBank achieves significant increases of accurate annotation rates of dansylated molecules, facilitating discovery of new hazardous pollutants from an environmental study of leather industrial wastewater. DeepCDM is also highly versatile for other classes of CDMs. Therefore, we envision that DeepCDM will pave a way for high-throughput identification of CDMs in non-targeted analysis to dig unknowns with potential health impacts from emerging anthropogenic chemicals.

Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things.

The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person's psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications.

Hypernetwork modeling and topology of high-order interactions for complex systems

Interactions among the underlying agents of a complex system are not only limited to dyads but can also occur in larger groups. Currently, no generic model has been developed to capture high-order interactions (HOI), which, along with pairwise interactions, portray a detailed landscape of complex systems. Here, we integrate evolutionary game theory and behavioral ecology into a unified statistical mechanics framework, allowing all agents (modeled as nodes) and their bidirectional, signed, and weighted interactions at various orders (modeled as links or hyperlinks) to be coded into hypernetworks. Such hypernetworks can distinguish between how pairwise interactions modulate a third agent (active HOI) and how the altered state of each agent in turn governs interactions between other agents (passive HOI). The simultaneous occurrence of active and passive HOI can drive complex systems to evolve at multiple time and space scales. We apply the model to reconstruct a hypernetwork of hexa-species microbial communities, and by dissecting the topological architecture of the hypernetwork using GLMY homology theory, we find distinct roles of pairwise interactions and HOI in shaping community behavior and dynamics. The statistical relevance of the hypernetwork model is validated using a series of in vitro mono-, co-, and tricultural experiments based on three bacterial species.

Sediment oxygen uptake and hypoxia in coastal oceans, the Pearl River Estuary region

Hypoxia is increasing in coastal oceans due to high oxygen consumption and weak ventilation. Quantifying biological oxygen uptake and how its effects on hypoxia respond to stratification is important for management and predicting future trends. This work introduces a simple analysis to quantify and compare the biological and physical drivers of hypoxia, by using an example from the Pearl River Estuary (PRE) region (10–70 m deep). We show that in the PRE region, sediment respires ∼50 % of organic matter produced in the water column and oxidizes ∼88 % of the ammonium produced from sediment organic matter degradation. These processes lead to high sediment oxygen uptake (SOU; 41.1 ± 16.3 mmol m-2d-1). Under stratification, sediment's effect on the bottom oxygen is strongly regulated by the thickness of the bottom boundary layer (BBL), and the robust relationships can be used to parameterize SOU. We then construct a simple and generic mass-balance model to estimate the water column oxygen uptake in the BBL (WOUBBL) and quantify the total oxygen loss. By comparing model results to observations in the PRE and other similar systems, we show that the sensitivity of oxygen levels to SOU is largely controlled by the duration of stratification, while the organic matter settling velocity controls the contributions of SOU to total oxygen consumption. We further demonstrate how the model can help evaluate the primary drivers of hypoxia (stratification versus high oxygen consumption), determine the time scale of stratification required to develop hypoxia, and explain the within and across system variabilities.

Elementary steps of catalytic reactions occurring on metallic alloy nanoparticles

Catalytic reactions occurring in an adsorbed overlayer on metallic alloy nanoparticles are of high interest in the context of applications in the chemical industry. The understanding of the corresponding kinetics is, however, still limited. One of the reasons of this state of the art is the interplay between adsorption and adsorbate‐influenced segregation of metal atoms inside alloy nanoparticles. I scrutinize this interplay by using a generic field model of segregation and the mean‐field approximation in order to describe adsorption, desorption, and elementary catalytic reactions. Under steady‐state conditions, the segregation is demonstrated to be manifested in the change of the dependence of the activation energies of desorption or elementary reactions on coverage, and the sign of this change is positive. The effect of this change on the apparent reaction orders is briefly discussed as well.

Validation of a generic finite element vehicle buck model for near-side crashes

Objective Finite element (FE) reconstructions of motor vehicle crashes using human body models are effective tools for developing a better understanding of occupant kinematics and injuries in real-world lateral crash conditions, but current near-side reconstruction methods are limited by the paucity of full-scale FE vehicle models. The objective of this study was to validate a generic vehicle model equipped with left-side airbags and intrusion capability by simulating a series of near-side crash tests for a range of vehicles and assessing model accuracy using objective evaluation methods. Methods Moving deformable barrier crash tests were reconstructed for five common vehicle classifications (compact passenger, mid-size passenger, sport utility vehicle, pickup truck, and van) using an updated version of a previously developed simplified vehicle model. Unknown vehicle and intrusion properties (pretensioner force, seatback airbag pressure, curtain airbag pressure, door panel stiffness, ratio of dynamic-to-static intrusion, intrusion velocity, and intrusion scaling factor) were estimated by parameterizing them across 224 simulations per crash test using a Latin hypercube design of experiments. Model accuracy was assessed for 13 anthropomorphic test device signals using the Correlation and Analysis (CORA) objective rating method and injury metric comparisons. Results Maximum ratings of 0.69, 0.67, 0.52, 0.52, and 0.62 were achieved for the compact passenger, midsize passenger, sport utility vehicle, pickup truck, and van classifications, respectively. On average, the abdomen displayed the most accurate behavior (0.51 ± 0.12), followed by the thorax (0.50 ± 0.10) and head (0.50 ± 0.07). The pelvis displayed the least accurate behavior (0.46 ± 0.18) of any region. Reconstructions overpraedicted injury metrics in all cases. Conclusions All vehicles achieved “fair” biofidelity ratings and the compact passenger and midsize passenger vehicles achieved “good” biofidelity ratings, validating them for kinematic evaluations with vehicle-to-vehicle nearside crash reconstructions. Regression models were developed for injuries and CORA ratings and can be used to optimize vehicle parameters in future studies.

Rewiring cattle movements to limit infection spread

Cattle tracing databases have become major resources for representing demographic processes of livestock and assessing potential risk of infections spreading by trade. The herds registered in these databases are nodes of a network of commercial movements, which can be altered to lower the risk of disease transmission. In this study, we develop an algorithm aimed at reducing the number of infected animals and herds, by rewiring specific movements responsible for trade flows from high- to low-prevalence herds. The algorithm is coupled with a generic computational model based on the French cattle movement tracing database (BDNI), and used to describe different scenarios for the spread of infection within and between herds from a recent outbreak (epidemic) or a five-year-old outbreak (endemic). Results show that rewiring successfully contains infections to a limited number of herds, especially if the outbreak is recent and the estimation of disease prevalence frequent, while the respective impact of the parameters of the algorithm depend on the infection parameters. Allowing any animal movement from high to low-prevalence herds reduces the effectiveness of the algorithm in epidemic settings, while frequent and fine-grained prevalence assessments improve the impact of the algorithm in endemic settings. Our approach focusing on a few commercial movements is expected to lead to substantial improvements in the control of a targeted disease, although changes in the network structure should be monitored for potential vulnerabilities to other diseases. This general algorithm could be applied to any network of controlled individual movements liable to spread disease.

Nonstationary A/B Tests: Optimal Variance Reduction, Bias Correction, and Valid Inference

We develop an analytical framework to appropriately model and adequately analyze A/B tests in presence of nonparametric nonstationarities in the targeted business metrics. A/B tests, also known as online randomized controlled experiments, have been used at scale by data-driven enterprises to guide decisions and test innovative ideas to improve core business metrics. Meanwhile, nonstationarities, such as the time-of-day effect and the day-of-week effect, can often arise nonparametrically in key business metrics involving purchases, revenue, conversions, customer experiences, and so on. First, we develop a generic nonparametric stochastic model to capture nonstationarities in A/B test experiments, where each sample represents a visit or action associated with a time label. We build a practically relevant limiting regime to facilitate analyzing large-sample estimator performances under nonparametric nonstationarities. Second, we show that ignoring or inadequately addressing nonstationarities can cause standard A/B test estimators to have suboptimal variance and nonvanishing bias, therefore leading to loss of statistical efficiency and accuracy. We provide a new estimator that views time as a continuous strata and performs poststratification with a data-dependent number of stratification levels. Without making parametric assumptions, we prove a central limit theorem for the proposed estimator and show that the estimator attains the best achievable asymptotic variance and is asymptotically unbiased. Third, we propose a time-grouped randomization that is designed to balance treatment and control assignments at granular time scales. We show that when the time-grouped randomization is integrated to standard experimental designs to generate experiment data, simple A/B test estimators can achieve asymptotically optimal variance. A brief account of numerical experiments are conducted to illustrate the analysis. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01205 .

Evaluation of a hyperspectral image pipeline toward building a generalisation capable crop dry matter content prediction model

Hyperspectral imaging has proven to be a reliable technique for estimating dry matter, a common variable when considering the quality of the fresh produce. However, developing models capable of generalising across different crops is challenging. In this study, several pipelines were explored towards achieving a robust and accurate generic regression model were evaluated and the development of Automatic Relevance Determination (ARD) and Partial Least Squares (PLS) algorithms for fruit and vegetable dry matter estimation. The models were built using a VIS-NIR dataset that includes both fruit and vegetables, namely, apples, broccoli and leek (n = 779). The PLS regression model obtained Root Mean Square on Prediction (RMSEP) = 0.0137, outperforming ARD regression (RMSEP = 0.0140) on a 10x5-fold cross-validation protocol. The evaluated preprocessing techniques affect the two regression algorithms differently, with the best results achieved when the pipeline was used without feature extraction. Overall, the pipeline using either ARD or PLS regression shows strong performance and generalisation for Visible-Near Infrared (VIS-NIR)-based dry matter estimation across diverse fruits and vegetables.

Projected state ensemble of a generic model of many-body quantum chaos

Abstract The projected ensemble is based on the study of the quantum state of a subsystem A conditioned on projective measurements in its complement. Recent studies have observed that a more refined measure of the thermalization of a chaotic quantum system can be defined on the basis of convergence of the projected ensemble to a quantum state design, i.e. a system thermalizes when it becomes indistinguishable, up to the kth moment, from a Haar ensemble of uniformly distributed pure states. Here we consider a random unitary circuit with the brick-wall geometry and analyze its convergence to the Haar ensemble through the frame potential and its mapping to a statistical mechanical problem. This approach allows us to highlight a geometric interpretation of the frame potential based on the existence of a fluctuating membrane, similar to those appearing in the study of entanglement entropies. At large local Hilbert space dimension q, we find that all moments converge simultaneously with a time scaling linearly in the size of region A, a feature previously observed in dual unitary models. However, based on the geometric interpretation, we argue that the scaling at finite q on the basis of rare membrane fluctuations, finding the logarithmic scaling of design times t k = O ( log ⁡ k ) . Our results are supported with numerical simulations performed at q = 2.

Density of the Subcritical Adsorbate on Gold Surfaces: A Generic Empirical Model

Density of the Subcritical Adsorbate on Gold Surfaces: A Generic Empirical Model

GeGra: Approaching a generic model for quantitative grain size analysis from materials microscopy data using deep learning

Grain size has a significant impact on the properties of materials, and is crucial for predicting material properties. Traditional grain size measurement relies heavily on human operators, leading to subjective results, and existing machine learning methods are typically material-specific, requiring significant labeling and training efforts for each new material. This paper provides insight into developing a deep learning-based generic grain boundary detection model (GeGra) from different material micrographs. The model is trained on 1006 images from various microscopy techniques such as light optical, Kerr, and scanning electron microscopy, acquired at different magnifications for different materials such as copper, austenite, brass, sintered hard magnet, hard metal, bronze, nickel silver, and aluminum. The developed GeGra model effectively handles visual artifacts and substructures such as twin grains, which often pose challenges for material-specific, state-of-the-art grain boundary segmentation models. The developed model achieved an IoU score of 69 % on a diverse test set and enables accurate grain size analysis using external image analysis software in less than one minute, according to ASTM standards, which is more than 5 times faster than the manual method. The developed model prioritizes generality with objective that it can have broader applicability for various materials instead of high-precision grain boundary detection. Additionally, the model has the potential to be a foundational tool for generalized grain size analysis in material microscopy, reducing the effort required for such analysis and assisting both material science experts and machine learning users.

Integration of proteomic data with genome-scale metabolic models: A methodological overview.

The integration of proteomics data with constraint-based reconstruction and analysis (COBRA) models plays a pivotal role in understanding the relationship between genotype and phenotype and bridges the gap between genome-level phenomena and functional adaptations. Integrating a generic genome-scale model with information on proteins enables generation of a context-specific metabolic model which improves the accuracy of model prediction. This review explores methodologies for incorporating proteomics data into genome-scale models. Available methods are grouped into four distinct categories based on their approach to integrate proteomics data and their depth of modeling. Within each category section various methods are introduced in chronological order of publication demonstrating the progress of this field. Furthermore, challenges and potential solutions to further progress are outlined, including the limited availability of appropriate in vitro data, experimental enzyme turnover rates, and the trade-off between model accuracy, computational tractability, and data scarcity. In conclusion, methods employing simpler approaches demand fewer kinetic and omics data, consequently leading to a less complex mathematical problem and reduced computational expenses. On the other hand, approaches that delve deeper into cellular mechanisms and aim to create detailed mathematical models necessitate more extensive kinetic and omics data, resulting in a more complex and computationally demanding problem. However, in some cases, this increased cost can be justified by the potential for more precise predictions.

Interaction-Driven Instabilities in the Random-Field XXZ Chain.

Despite enormous efforts devoted to the study of the many-body localization (MBL) phenomenon, the nature of the high-energy behavior of the Heisenberg spin chain in a strong random magnetic field is lacking consensus. Here, we take a step back by exploring the weak interaction limit starting from the Anderson localized (AL) insulator. Through shift-invert diagonalization, we find that, below a certain disorder threshold h^{*}, weak interactions necessarily lead to an ergodic instability, whereas at strong disorder the AL insulator directly turns into MBL, in agreement with a simple interpretation of the avalanche theory for restoration of ergodicity. We further map the phase diagram for the generic XXZ model in the disorder h-interaction Δ plane. Taking advantage of the total magnetization conservation, our results unveil the remarkable behavior of the spin-spin correlation functions: in the regime indicated as MBL by standard observables, their exponential decay undergoes an inversion of orientation ξ_{z}>ξ_{x}. We find that the longitudinal length ξ_{z} is a key quantity for capturing ergodic instabilities, as it increases with system size near the thermal phase, in sharp contrast to its transverse counterpart.

Insights into muscle metabolic energetics: Modelling muscle-tendon mechanics and metabolic rates during walking across speeds.

The metabolic energy rate of individual muscles is impossible to measure without invasive procedures. Prior studies have produced models to predict metabolic rates based on experimental observations of isolated muscle contraction from various species. Such models can provide reliable predictions of metabolic rates in humans if muscle properties and control are accurately modeled. This study aimed to examine how muscle-tendon model individualization and metabolic energy models influenced estimation of muscle-tendon states and time-series metabolic rates, to evaluate the agreement with empirical data, and to provide predictions of the metabolic rate of muscle groups and gait phases across walking speeds. Three-dimensional musculoskeletal simulations with prescribed kinematics and dynamics were performed. An optimal control formulation was used to compute muscle-tendon states with four levels of individualization, ranging from a scaled generic model and muscle controls based on minimal activations, inclusion of calibrated muscle passive forces, personalization of Achilles and quadriceps tendon stiffnesses, to finally informing muscle controls with electromyography. We computed metabolic rates based on existing models. Simulations with calibrated passive forces and personalized tendon stiffness most accurately estimate muscle excitations and fiber lengths. Interestingly, the inclusion of electromyography did not improve our estimates. The whole-body average metabolic cost was better estimated with a subset of metabolic energy models. We estimated metabolic rate peaks near early stance, pre-swing, and initial swing at all walking speeds. Plantarflexors accounted for the highest cost among muscle groups at the preferred speed and were similar to the cost of hip adductors and abductors combined. Also, the swing phase accounted for slightly more than one-quarter of the total cost in a gait cycle, and its relative cost decreased with walking speed. Our prediction might inform the design of assistive devices and rehabilitation treatment. The code and experimental data are available online.

Self-sovereign identity management in ciphertext policy attribute based encryption for IoT protocols

In the Internet of Things, access control and identity management rely on centralized platforms. However, centralized platforms will compromise user privacy with identity leakage. Self-sovereign identity (SSI) is a novel model for identity management that does not require third-party centralized authority. Thus, SSI is a potential solution to the identity management problem in IoT access control. This paper’s motivation is to address the problems of lack of identity sovereignty, centralized authorization, and high computational overhead for IoT access control. We propose a novel access control scheme for IoT that decentralizes identity management and tackles single-point-of-failure issues. This scheme leverages ciphertext policy attribute-based encryption (CP-ABE) and SSI to achieve the overall goal. Specifically, Our scheme eliminates the central authority and empowers users to manage their identity, allowing users to decide what attributes they disclose. Regarding the distribution of roles in the architecture, this paper follows the generic SSI model (ISSUER–HOLDER—VERIFIER) that allows a user to access a service from a service provider. To enable real-world deployment of our scheme, we establish an attribute authorization authority(such as the government) as a trusted identity point of entry. Users generate decentralized identifiers to enjoy services of interest in a privacy-preserving manner. The analysis demonstrates the practicality and superiority of our scheme. Our scheme requires less computation and is suitable for resource-constrained IoT scenarios.

Resonant multiscalar production in the generic complex singlet model in the multi-TeV region

We develop benchmarks for resonant discalar production in the generic complex singlet scalar extension of the Standard Model (SM) with no additional symmetries, which contains two new scalars. These benchmarks maximize discalar resonant production modes at future pp colliders: pp→h2→h1h1, pp→h2→h1h3, and pp→h2→h3h3, where h1 is the observed SM-like Higgs boson and h2,3 are new scalars. The decays h2→h1h3 and h2→h3h3 may be the only way to discover h3, leading to a discovery of two new scalars at once. Current LHC and projected future collider (HL-LHC, FCC-ee+HL-LHC, ILC500+HL-LHC) constraints on this model are used to produce benchmarks at the HL-LHC for h2 masses between 250 GeV and 1 TeV and a future pp collider (FCC-hh) for h2 masses between 250 GeV and 12 TeV. We update the current LHC bounds on the singlet-Higgs boson mixing angle for these benchmarks. As the mass of h2 approaches the multi-TeV region, certain limiting behaviors of the maximum rates are uncovered due to theoretical constraints on the parameters. These limits, which can be derived analytically, are BR(h2→h1h1)→0.25, BR(h2→h3h3)→0.5, and BR(h2→h1h3)→0. It can also be shown that the maximum rates of pp→h2→h1h1 and pp→h2→h3h3 approach the same value. Hence, all three h2→hihj decays are promising discovery modes for h2 masses at and below O(1 TeV), while above O(1 TeV) the decays h2→h1h1 and h2→h3h3 are more encouraging. We choose benchmark masses for h3 to produce a large range of decay signatures including multi-b, multivector boson, and multi-SM-like Higgs production. As we will show, the behavior of the maximum rates leads to the surprising conclusion that in the multi-TeV region this model may be discovered in the Higgs quartet production mode via h2→h3h3→4h1 decays before Higgs triple production is observed. The maximum di- and four Higgs production rates are similar in the multi-TeV range. Published by the American Physical Society 2024

Efficient Highlighting of Visual Targets in Complex Natural Scenes

Guided visual search is a common theme in HF applications. In this project we use large-scale databases of wildlife camera-trap imagery as a testbed for optimization of target highlighting. MegaDetector, a generic animal detection deep machine learning model, provides bounding boxes for potential targets within an image. In some cases, human observers are necessary to confirm or further classify the detections. Outlining the bounding box can direct human attention to the target AOI to improve observers’ classification speed and accuracy. However, this outline introduces visual clutter and crowding at the AOI boundary. In a first empirical study we investigate the use of padding to mitigate the effects of local clutter and compared different methods of visual highlighting (colored outline vs. blur outside of AOI). We found support for using padding to improve performance when animals were hard to see. Both colored outlines and blur were effective at directing observers’ attention.

Neurological Manifestations and Complications of Neonatal Meningitis: A Systematic Review and Meta-analysis

Background: Neonatal meningitis has significant worldwide morbidity and mortality rates. Nevertheless, the clinical manifestations of neonatal meningitis are subtle and nonspecific which can pose challenges in terms of timely diagnosis and intervention. This meta-analysis aims to the prevalence of neurological manifestations and complications associated with neonatal meningitis in term and preterm neonates. Methods: A systematic literature search was conducted in PubMed, SCOPUS, and Ovid SP to identify relevant studies. Pooled prevalence of neurological manifestations and complications with a 95% confidence interval (CI) were calculated using the random-effects generic inverse variance model. Statistical analysis was performed using Review Manager v5.4 and R programming. Results: A total of 25 studies with 2252 patients evaluated for neurological symptoms and 1822 patients evaluated for neurological complications were included in this meta-analysis. The overall prevalence of neurological manifestations in neonatal meningitis was 23% (95% CI, 20% to 26%). The overall prevalence of neurological complications in neonatal meningitis was 8% (95% CI, 6% to 9%). Conclusion: Our meta-analysis revealed a low prevalence of neurological manifestations and complications in neonatal meningitis. Although neurological complications occur less frequently, the complications are significantly associated with higher mortality and morbidity rates.

Research on wind turbine blade coating erosion modeling based on Generic modeling

Abstract In order to improve the accuracy of the prediction of the erosion phenomenon of wind turbine blade coatings, this study incorporates the wind-sand erosion data of wind turbine blade coating flat specimens obtained through wind tunnel experiments on the basis of the widely used generalized erosion model. A prediction model that accurately reflects the erosion and wear characteristics of wind turbine blade coatings is constructed using the least squares method. All the function parts included in the model showed favorable fitting effects, and the error between the model prediction results and the measured data was limited to a tolerable range. Therefore, the model has proved to be effective in predicting the erosion rate of wind turbine blade coating materials, which provides a solid theoretical foundation for the numerical simulation of the erosion and wear of wind turbine blades in a two-phase wind-sand flow environment.